"""
GPU Acceleration System for Deep Learning Models
High-performance GPU computing for stock prediction models
"""

import numpy as np
import pandas as pd
from typing import Dict, List, Tuple, Optional, Any, Union
import time
import warnings
warnings.filterwarnings('ignore')

try:
    import torch
    import torch.nn as nn
    import torch.optim as optim
    from torch.utils.data import DataLoader, TensorDataset
    from torch.cuda.amp import GradScaler, autocast
    PYTORCH_AVAILABLE = True
except ImportError:
    PYTORCH_AVAILABLE = False

try:
    import tensorflow as tf
    from tensorflow.keras.models import Sequential, Model
    from tensorflow.keras.layers import Dense, LSTM, GRU, Dropout, BatchNormalization, Attention
    from tensorflow.keras.optimizers import Adam, RMSprop
    from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
    from tensorflow.keras.mixed_precision import set_global_policy
    TENSORFLOW_AVAILABLE = True
except ImportError:
    TENSORFLOW_AVAILABLE = False

try:
    import cupy as cp
    import cudf
    import cuml
    from cuml.ensemble import RandomForestRegressor as cuRF
    from cuml.linear_model import Ridge as cuRidge
    RAPIDS_AVAILABLE = True
except ImportError:
    RAPIDS_AVAILABLE = False

try:
    import numba
    from numba import cuda, jit
    NUMBA_CUDA_AVAILABLE = True
except ImportError:
    NUMBA_CUDA_AVAILABLE = False

class GPUDetector:
    """
    Detect and configure available GPU resources
    """
    
    def __init__(self):
        self.gpu_available = False
        self.gpu_count = 0
        self.gpu_memory = []
        self.gpu_names = []
        self.framework_support = {}
        
        self._detect_hardware()
        self._check_framework_support()
    
    def _detect_hardware(self):
        """Detect available GPU hardware"""
        
        # Check CUDA availability
        if PYTORCH_AVAILABLE:
            self.gpu_available = torch.cuda.is_available()
            if self.gpu_available:
                self.gpu_count = torch.cuda.device_count()
                for i in range(self.gpu_count):
                    props = torch.cuda.get_device_properties(i)
                    self.gpu_names.append(props.name)
                    self.gpu_memory.append(props.total_memory // 1024**3)  # GB
        
        # Check TensorFlow GPU
        if TENSORFLOW_AVAILABLE:
            gpus = tf.config.experimental.list_physical_devices('GPU')
            if gpus and not self.gpu_available:
                self.gpu_available = True
                self.gpu_count = len(gpus)
        
        # Check CuPy
        if RAPIDS_AVAILABLE:
            try:
                cp.cuda.runtime.getDeviceCount()
                if not self.gpu_available:
                    self.gpu_available = True
            except:
                pass
    
    def _check_framework_support(self):
        """Check which frameworks support GPU"""
        self.framework_support = {
            'pytorch': PYTORCH_AVAILABLE and torch.cuda.is_available(),
            'tensorflow': TENSORFLOW_AVAILABLE and len(tf.config.experimental.list_physical_devices('GPU')) > 0,
            'rapids': RAPIDS_AVAILABLE,
            'numba_cuda': NUMBA_CUDA_AVAILABLE,
            'cupy': RAPIDS_AVAILABLE
        }
    
    def get_gpu_info(self) -> Dict[str, Any]:
        """Get comprehensive GPU information"""
        return {
            'gpu_available': self.gpu_available,
            'gpu_count': self.gpu_count,
            'gpu_memory': self.gpu_memory,
            'gpu_names': self.gpu_names,
            'framework_support': self.framework_support
        }
    
    def print_gpu_info(self):
        """Print GPU information"""
        print("🖥️ GPU HARDWARE DETECTION")
        print("=" * 40)
        
        if self.gpu_available:
            print(f"✅ GPU Available: {self.gpu_count} device(s)")
            for i, (name, memory) in enumerate(zip(self.gpu_names, self.gpu_memory)):
                print(f"  GPU {i}: {name} ({memory}GB)")
            
            print(f"\n📦 Framework Support:")
            for framework, supported in self.framework_support.items():
                status = "✅" if supported else "❌"
                print(f"  {status} {framework.title()}")
        else:
            print("❌ No GPU detected - using CPU fallback")

class PyTorchGPUAccelerator:
    """
    GPU acceleration for PyTorch models
    """
    
    def __init__(self, device: str = None):
        self.device = device or ('cuda' if torch.cuda.is_available() else 'cpu')
        self.scaler = GradScaler() if self.device == 'cuda' else None
        
        print(f"🚀 PyTorch accelerator initialized on {self.device}")
    
    def create_gpu_lstm_model(self, 
                             input_size: int,
                             hidden_size: int = 128,
                             num_layers: int = 2,
                             output_size: int = 1,
                             dropout: float = 0.2) -> nn.Module:
        """Create GPU-optimized LSTM model"""
        
        class GPULSTMModel(nn.Module):
            def __init__(self, input_size, hidden_size, num_layers, output_size, dropout):
                super(GPULSTMModel, self).__init__()
                
                self.hidden_size = hidden_size
                self.num_layers = num_layers
                
                # LSTM layers
                self.lstm = nn.LSTM(
                    input_size, 
                    hidden_size, 
                    num_layers, 
                    batch_first=True,
                    dropout=dropout if num_layers > 1 else 0,
                    bidirectional=True
                )
                
                # Attention mechanism
                self.attention = nn.MultiheadAttention(
                    hidden_size * 2, 8, batch_first=True
                )
                
                # Fully connected layers
                self.fc1 = nn.Linear(hidden_size * 2, hidden_size)
                self.dropout = nn.Dropout(dropout)
                self.fc2 = nn.Linear(hidden_size, hidden_size // 2)
                self.fc3 = nn.Linear(hidden_size // 2, output_size)
                
                # Batch normalization
                self.bn1 = nn.BatchNorm1d(hidden_size)
                self.bn2 = nn.BatchNorm1d(hidden_size // 2)
                
                # Activation
                self.relu = nn.ReLU()
                self.leaky_relu = nn.LeakyReLU(0.01)
            
            def forward(self, x):
                batch_size = x.size(0)
                
                # LSTM forward pass
                lstm_out, _ = self.lstm(x)
                
                # Apply attention
                attn_out, _ = self.attention(lstm_out, lstm_out, lstm_out)
                
                # Take last sequence output
                last_output = attn_out[:, -1, :]
                
                # Fully connected layers
                out = self.fc1(last_output)
                out = self.bn1(out)
                out = self.leaky_relu(out)
                out = self.dropout(out)
                
                out = self.fc2(out)
                out = self.bn2(out)
                out = self.relu(out)
                out = self.dropout(out)
                
                out = self.fc3(out)
                
                return out
        
        model = GPULSTMModel(input_size, hidden_size, num_layers, output_size, dropout)
        return model.to(self.device)
    
    def create_gpu_transformer_model(self,
                                   input_size: int,
                                   d_model: int = 128,
                                   nhead: int = 8,
                                   num_layers: int = 6,
                                   output_size: int = 1) -> nn.Module:
        """Create GPU-optimized Transformer model"""
        
        class GPUTransformerModel(nn.Module):
            def __init__(self, input_size, d_model, nhead, num_layers, output_size):
                super(GPUTransformerModel, self).__init__()
                
                # Input projection
                self.input_projection = nn.Linear(input_size, d_model)
                
                # Positional encoding
                self.pos_encoding = self._create_positional_encoding(d_model)
                
                # Transformer encoder
                encoder_layer = nn.TransformerEncoderLayer(
                    d_model=d_model,
                    nhead=nhead,
                    dim_feedforward=d_model * 4,
                    dropout=0.1,
                    batch_first=True
                )
                self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
                
                # Output layers
                self.global_pool = nn.AdaptiveAvgPool1d(1)
                self.classifier = nn.Sequential(
                    nn.Linear(d_model, d_model // 2),
                    nn.ReLU(),
                    nn.Dropout(0.1),
                    nn.Linear(d_model // 2, output_size)
                )
            
            def _create_positional_encoding(self, d_model, max_len=5000):
                pe = torch.zeros(max_len, d_model)
                position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
                
                div_term = torch.exp(torch.arange(0, d_model, 2).float() * 
                                   (-np.log(10000.0) / d_model))
                
                pe[:, 0::2] = torch.sin(position * div_term)
                pe[:, 1::2] = torch.cos(position * div_term)
                
                return pe.unsqueeze(0)
            
            def forward(self, x):
                seq_len = x.size(1)
                
                # Input projection
                x = self.input_projection(x)
                
                # Add positional encoding
                pos_enc = self.pos_encoding[:, :seq_len, :].to(x.device)
                x = x + pos_enc
                
                # Transformer
                x = self.transformer(x)
                
                # Global pooling and classification
                x = x.transpose(1, 2)  # (batch, features, seq_len)
                x = self.global_pool(x).squeeze(-1)  # (batch, features)
                x = self.classifier(x)
                
                return x
        
        model = GPUTransformerModel(input_size, d_model, nhead, num_layers, output_size)
        return model.to(self.device)
    
    def train_model_gpu(self,
                       model: nn.Module,
                       train_loader: DataLoader,
                       val_loader: DataLoader,
                       epochs: int = 100,
                       learning_rate: float = 0.001,
                       patience: int = 10) -> Dict[str, List[float]]:
        """Train model with GPU acceleration and mixed precision"""
        
        model = model.to(self.device)
        criterion = nn.MSELoss()
        optimizer = optim.AdamW(model.parameters(), lr=learning_rate, weight_decay=1e-5)
        scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, patience=5, factor=0.5)
        
        train_losses = []
        val_losses = []
        best_val_loss = float('inf')
        patience_counter = 0
        
        for epoch in range(epochs):
            # Training phase
            model.train()
            train_loss = 0.0
            
            for batch_x, batch_y in train_loader:
                batch_x, batch_y = batch_x.to(self.device), batch_y.to(self.device)
                
                optimizer.zero_grad()
                
                if self.scaler:  # Mixed precision training
                    with autocast():
                        outputs = model(batch_x)
                        loss = criterion(outputs.squeeze(), batch_y)
                    
                    self.scaler.scale(loss).backward()
                    self.scaler.step(optimizer)
                    self.scaler.update()
                else:
                    outputs = model(batch_x)
                    loss = criterion(outputs.squeeze(), batch_y)
                    loss.backward()
                    optimizer.step()
                
                train_loss += loss.item()
            
            # Validation phase
            model.eval()
            val_loss = 0.0
            
            with torch.no_grad():
                for batch_x, batch_y in val_loader:
                    batch_x, batch_y = batch_x.to(self.device), batch_y.to(self.device)
                    
                    if self.scaler:
                        with autocast():
                            outputs = model(batch_x)
                            loss = criterion(outputs.squeeze(), batch_y)
                    else:
                        outputs = model(batch_x)
                        loss = criterion(outputs.squeeze(), batch_y)
                    
                    val_loss += loss.item()
            
            # Average losses
            train_loss /= len(train_loader)
            val_loss /= len(val_loader)
            
            train_losses.append(train_loss)
            val_losses.append(val_loss)
            
            # Learning rate scheduling
            scheduler.step(val_loss)
            
            # Early stopping
            if val_loss < best_val_loss:
                best_val_loss = val_loss
                patience_counter = 0
            else:
                patience_counter += 1
                
            if patience_counter >= patience:
                print(f"Early stopping at epoch {epoch+1}")
                break
            
            # Progress reporting
            if (epoch + 1) % 10 == 0:
                print(f"Epoch {epoch+1}: Train Loss: {train_loss:.6f}, Val Loss: {val_loss:.6f}")
        
        return {
            'train_losses': train_losses,
            'val_losses': val_losses,
            'best_val_loss': best_val_loss
        }

class TensorFlowGPUAccelerator:
    """
    GPU acceleration for TensorFlow models
    """
    
    def __init__(self):
        self.setup_gpu()
    
    def setup_gpu(self):
        """Setup TensorFlow GPU configuration"""
        if TENSORFLOW_AVAILABLE:
            gpus = tf.config.experimental.list_physical_devices('GPU')
            if gpus:
                try:
                    # Enable memory growth
                    for gpu in gpus:
                        tf.config.experimental.set_memory_growth(gpu, True)
                    
                    # Enable mixed precision
                    set_global_policy('mixed_float16')
                    
                    print(f"✅ TensorFlow GPU configured: {len(gpus)} GPU(s)")
                except RuntimeError as e:
                    print(f"⚠️ GPU configuration error: {e}")
    
    def create_gpu_lstm_model(self,
                             input_shape: Tuple[int, int],
                             output_size: int = 1) -> Model:
        """Create GPU-optimized LSTM model in TensorFlow"""
        
        model = Sequential([
            # Input layer
            tf.keras.layers.InputLayer(input_shape=input_shape),
            
            # LSTM layers
            LSTM(128, return_sequences=True, recurrent_dropout=0.2),
            BatchNormalization(),
            LSTM(64, return_sequences=True, recurrent_dropout=0.2),
            BatchNormalization(),
            LSTM(32, return_sequences=False, recurrent_dropout=0.2),
            
            # Dense layers
            Dense(64, activation='relu'),
            Dropout(0.3),
            BatchNormalization(),
            Dense(32, activation='relu'),
            Dropout(0.2),
            Dense(output_size, activation='linear', dtype='float32')  # Ensure float32 output
        ])
        
        # Use mixed precision compatible optimizer
        optimizer = Adam(learning_rate=0.001)
        
        model.compile(
            optimizer=optimizer,
            loss='mse',
            metrics=['mae']
        )
        
        return model
    
    def create_gpu_transformer_model(self,
                                   input_shape: Tuple[int, int],
                                   output_size: int = 1) -> Model:
        """Create GPU-optimized Transformer model"""
        
        inputs = tf.keras.Input(shape=input_shape)
        
        # Multi-head attention
        attention_output = tf.keras.layers.MultiHeadAttention(
            num_heads=8,
            key_dim=64
        )(inputs, inputs)
        
        # Add & Norm
        attention_output = tf.keras.layers.Add()([inputs, attention_output])
        attention_output = tf.keras.layers.LayerNormalization()(attention_output)
        
        # Feed forward
        ff_output = tf.keras.layers.Dense(256, activation='relu')(attention_output)
        ff_output = tf.keras.layers.Dense(input_shape[-1])(ff_output)
        
        # Add & Norm
        ff_output = tf.keras.layers.Add()([attention_output, ff_output])
        ff_output = tf.keras.layers.LayerNormalization()(ff_output)
        
        # Global pooling
        pooled = tf.keras.layers.GlobalAveragePooling1D()(ff_output)
        
        # Output layers
        outputs = tf.keras.layers.Dense(128, activation='relu')(pooled)
        outputs = tf.keras.layers.Dropout(0.2)(outputs)
        outputs = tf.keras.layers.Dense(64, activation='relu')(outputs)
        outputs = tf.keras.layers.Dropout(0.1)(outputs)
        outputs = tf.keras.layers.Dense(output_size, activation='linear', dtype='float32')(outputs)
        
        model = Model(inputs=inputs, outputs=outputs)
        
        model.compile(
            optimizer=Adam(learning_rate=0.001),
            loss='mse',
            metrics=['mae']
        )
        
        return model
    
    def train_model_gpu(self,
                       model: Model,
                       X_train: np.ndarray,
                       y_train: np.ndarray,
                       X_val: np.ndarray,
                       y_val: np.ndarray,
                       epochs: int = 100,
                       batch_size: int = 32) -> tf.keras.callbacks.History:
        """Train model with GPU acceleration"""
        
        callbacks = [
            EarlyStopping(patience=15, restore_best_weights=True, monitor='val_loss'),
            ReduceLROnPlateau(patience=7, factor=0.5, min_lr=1e-7, monitor='val_loss')
        ]
        
        history = model.fit(
            X_train, y_train,
            validation_data=(X_val, y_val),
            epochs=epochs,
            batch_size=batch_size,
            callbacks=callbacks,
            verbose=1
        )
        
        return history

class RAPIDSGPUAccelerator:
    """
    GPU acceleration using RAPIDS (cuML, cuDF)
    """
    
    def __init__(self):
        self.available = RAPIDS_AVAILABLE
        if self.available:
            print("✅ RAPIDS GPU acceleration available")
        else:
            print("❌ RAPIDS not available")
    
    def gpu_dataframe_operations(self, df: pd.DataFrame) -> pd.DataFrame:
        """Accelerate DataFrame operations with cuDF"""
        if not self.available:
            return df
        
        # Convert to GPU DataFrame
        gdf = cudf.from_pandas(df)
        
        # Perform GPU-accelerated operations
        # Example: feature scaling, calculations, etc.
        
        # Convert back to pandas
        return gdf.to_pandas()
    
    def gpu_random_forest(self,
                         X_train: np.ndarray,
                         y_train: np.ndarray,
                         **kwargs) -> Any:
        """Train Random Forest on GPU"""
        if not self.available:
            return None
        
        # Convert to GPU arrays
        X_train_gpu = cp.asarray(X_train)
        y_train_gpu = cp.asarray(y_train)
        
        # Create and train GPU Random Forest
        rf_gpu = cuRF(**kwargs)
        rf_gpu.fit(X_train_gpu, y_train_gpu)
        
        return rf_gpu
    
    def gpu_ridge_regression(self,
                           X_train: np.ndarray,
                           y_train: np.ndarray,
                           **kwargs) -> Any:
        """Train Ridge Regression on GPU"""
        if not self.available:
            return None
        
        # Convert to GPU arrays
        X_train_gpu = cp.asarray(X_train)
        y_train_gpu = cp.asarray(y_train)
        
        # Create and train GPU Ridge Regression
        ridge_gpu = cuRidge(**kwargs)
        ridge_gpu.fit(X_train_gpu, y_train_gpu)
        
        return ridge_gpu

class NumbaGPUAccelerator:
    """
    GPU acceleration using Numba CUDA
    """
    
    def __init__(self):
        self.available = NUMBA_CUDA_AVAILABLE
        if self.available:
            print("✅ Numba CUDA acceleration available")
        else:
            print("❌ Numba CUDA not available")
    
    @staticmethod
    @cuda.jit if NUMBA_CUDA_AVAILABLE else lambda x: x
    def gpu_matrix_multiply(A, B, C):
        """GPU matrix multiplication kernel"""
        i, j = cuda.grid(2)
        if i < C.shape[0] and j < C.shape[1]:
            tmp = 0.0
            for k in range(A.shape[1]):
                tmp += A[i, k] * B[k, j]
            C[i, j] = tmp
    
    @staticmethod
    @cuda.jit if NUMBA_CUDA_AVAILABLE else lambda x: x
    def gpu_elementwise_operation(arr, result):
        """GPU elementwise operations kernel"""
        i = cuda.grid(1)
        if i < arr.size:
            result[i] = arr[i] * arr[i] + 2.0 * arr[i] + 1.0

class GPUBenchmark:
    """
    Benchmark GPU performance vs CPU
    """
    
    def __init__(self):
        self.detector = GPUDetector()
    
    def benchmark_matrix_operations(self, size: int = 2048) -> Dict[str, float]:
        """Benchmark matrix operations"""
        results = {}
        
        # Generate test data
        A = np.random.randn(size, size).astype(np.float32)
        B = np.random.randn(size, size).astype(np.float32)
        
        # CPU benchmark
        start_time = time.time()
        C_cpu = np.dot(A, B)
        cpu_time = time.time() - start_time
        results['cpu_time'] = cpu_time
        
        # GPU benchmarks
        if PYTORCH_AVAILABLE and torch.cuda.is_available():
            # PyTorch GPU
            A_torch = torch.from_numpy(A).cuda()
            B_torch = torch.from_numpy(B).cuda()
            
            torch.cuda.synchronize()
            start_time = time.time()
            C_torch = torch.mm(A_torch, B_torch)
            torch.cuda.synchronize()
            pytorch_time = time.time() - start_time
            results['pytorch_gpu_time'] = pytorch_time
            results['pytorch_speedup'] = cpu_time / pytorch_time
        
        if RAPIDS_AVAILABLE:
            # CuPy GPU
            A_cupy = cp.asarray(A)
            B_cupy = cp.asarray(B)
            
            cp.cuda.Stream.null.synchronize()
            start_time = time.time()
            C_cupy = cp.dot(A_cupy, B_cupy)
            cp.cuda.Stream.null.synchronize()
            cupy_time = time.time() - start_time
            results['cupy_gpu_time'] = cupy_time
            results['cupy_speedup'] = cpu_time / cupy_time
        
        return results
    
    def benchmark_model_training(self) -> Dict[str, Any]:
        """Benchmark model training speed"""
        
        # Generate synthetic data
        n_samples = 10000
        n_features = 100
        sequence_length = 50
        
        X = np.random.randn(n_samples, sequence_length, n_features).astype(np.float32)
        y = np.random.randn(n_samples).astype(np.float32)
        
        results = {}
        
        # PyTorch GPU benchmark
        if PYTORCH_AVAILABLE and torch.cuda.is_available():
            accelerator = PyTorchGPUAccelerator()
            model = accelerator.create_gpu_lstm_model(n_features, 64, 2)
            
            # Create data loader
            dataset = TensorDataset(torch.from_numpy(X), torch.from_numpy(y))
            train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
            val_loader = DataLoader(dataset, batch_size=32)
            
            start_time = time.time()
            training_results = accelerator.train_model_gpu(
                model, train_loader, val_loader, epochs=5
            )
            pytorch_time = time.time() - start_time
            
            results['pytorch_training_time'] = pytorch_time
            results['pytorch_final_loss'] = training_results['best_val_loss']
        
        # TensorFlow GPU benchmark
        if TENSORFLOW_AVAILABLE:
            accelerator = TensorFlowGPUAccelerator()
            model = accelerator.create_gpu_lstm_model((sequence_length, n_features))
            
            start_time = time.time()
            history = accelerator.train_model_gpu(
                model, X, y, X[:1000], y[:1000], epochs=5, batch_size=32
            )
            tensorflow_time = time.time() - start_time
            
            results['tensorflow_training_time'] = tensorflow_time
            results['tensorflow_final_loss'] = min(history.history['val_loss'])
        
        return results
    
    def print_benchmark_results(self):
        """Print comprehensive benchmark results"""
        print("\n🏁 GPU PERFORMANCE BENCHMARK")
        print("=" * 50)
        
        # Hardware info
        self.detector.print_gpu_info()
        
        # Matrix operations benchmark
        print(f"\n⚡ Matrix Operations Benchmark (2048x2048)")
        matrix_results = self.benchmark_matrix_operations()
        
        for operation, time_taken in matrix_results.items():
            if 'time' in operation:
                print(f"  {operation}: {time_taken:.4f}s")
            elif 'speedup' in operation:
                print(f"  {operation}: {time_taken:.1f}x faster")
        
        # Model training benchmark
        print(f"\n🤖 Model Training Benchmark")
        training_results = self.benchmark_model_training()
        
        for metric, value in training_results.items():
            if 'time' in metric:
                print(f"  {metric}: {value:.2f}s")
            elif 'loss' in metric:
                print(f"  {metric}: {value:.6f}")

# Example usage and testing
if __name__ == "__main__":
    print("🚀 GPU Acceleration System")
    print("=" * 40)
    
    # Initialize GPU detector
    detector = GPUDetector()
    detector.print_gpu_info()
    
    # Run benchmarks
    benchmark = GPUBenchmark()
    benchmark.print_benchmark_results()
    
    # Test PyTorch GPU acceleration
    if PYTORCH_AVAILABLE and torch.cuda.is_available():
        print(f"\n🔥 Testing PyTorch GPU Acceleration...")
        
        accelerator = PyTorchGPUAccelerator()
        
        # Create sample data
        n_samples = 1000
        sequence_length = 30
        n_features = 20
        
        X = np.random.randn(n_samples, sequence_length, n_features).astype(np.float32)
        y = np.random.randn(n_samples).astype(np.float32)
        
        # Create model
        model = accelerator.create_gpu_lstm_model(n_features, hidden_size=64)
        
        # Create data loaders
        dataset = TensorDataset(torch.from_numpy(X), torch.from_numpy(y))
        train_loader = DataLoader(dataset, batch_size=32, shuffle=True)
        val_loader = DataLoader(dataset, batch_size=32)
        
        # Train model
        print("Training GPU model...")
        results = accelerator.train_model_gpu(
            model, train_loader, val_loader, epochs=5
        )
        
        print(f"✅ Training completed!")
        print(f"Final validation loss: {results['best_val_loss']:.6f}")
    
    # Test TensorFlow GPU acceleration
    if TENSORFLOW_AVAILABLE:
        print(f"\n🔥 Testing TensorFlow GPU Acceleration...")
        
        accelerator = TensorFlowGPUAccelerator()
        
        # Create sample data
        X = np.random.randn(1000, 30, 20).astype(np.float32)
        y = np.random.randn(1000).astype(np.float32)
        
        # Create model
        model = accelerator.create_gpu_lstm_model((30, 20))
        
        print("Training TensorFlow GPU model...")
        history = accelerator.train_model_gpu(
            model, X, y, X[:200], y[:200], epochs=5, batch_size=32
        )
        
        print(f"✅ TensorFlow training completed!")
        print(f"Final validation loss: {min(history.history['val_loss']):.6f}")
    
    print(f"\n🎯 GPU acceleration system ready!")